The present invention relates to the digital imaging technology field, in particular, it relates to a method for acquiring/controlling automatic exposure control parameters and an imaging device.
In the process of shooting a video, adequate exposure of a photographing subject is an important condition in order to obtain satisfactory image quality. The existing imaging device commonly adopts two control methods: the manual exposure method and automatic exposure method. With the manual exposure method, adequate amount of exposure is achieved through manual adjustment of the aperture and shutter speed. This method allows a photographer to adjust the amount of exposure according to his, particular aesthetic needs. Thus, this method has been widely used in the area of professional photography and video photography. In contrast, the automatic exposure control method is generally used in everyday non-professional devices such as your common household digital camera, home video camera, etc.
The exposure control for video camera devices using automatic exposure control is designed generally based on “Gray World Assumption”; the average brightness (AY) of the current image is taken as the exposure control parameter, and through adjusting shutter speed, aperture size, and amplifier gain, AY approaches the image average brightness reference value (AYref) that is setup. Taking AY as the exposure control parameter certainly can achieve a more ideal effect for the scenes conforming with “Gray World Assumption”; however, the “special scenes” with a large section of dark area or a large section of bright area are no longer considered as a “Gray World”, and taking AY to control automatic exposure will generate phenomena such as the over exposure or under exposure of the photograph subject. For example, in scene 1, a person wearing a yellow outfit is posing for a picture with snow in the background. Because the background and the outfit both have large sections of high brightness areas, which causes AY to become too high, therefore, automatic exposure control carries out substantial adjustment to reduce light flux (shutter speed, aperture size, and amplifier gain, etc.), which leads to serious insufficient exposure of the face of the subject. In scene 2, a person wearing a black outfit is posing for a picture with a brown curtain as the background. This case is completely opposite to Scene 1. The result of taking AY as the exposure control parameters results in significant over exposure of the subject's face.
Toshinobu Haruki and others provided a method for reducing under exposure or over exposure for subjects photographed under “special scenes” in the paper “Video camera system using fuzzy logic” (Toshinobu Haruki, Kenichi kikuchi, AV Development Center, Sanyo Electric Co., Ltd. “Video camera system using fuzzy logic”, IEEE Transactions on Consumer Electronics, Vol. 38, No. 3, AUGUST 1992. The main idea of this method is to apply a certain fuzzy control strategy to reduce the weight of the non-subject area in AY computing, based on the position of the proposed photograph subject in the picture, thereby reducing the effect of exposure control on the non-subject image. The typical application scenes used in this technical proposal are head and shoulder shots, and we further assumed that all the photograph subjects were generally located in the middle or the lower portion of the picture. Thus, greater weight was given to these areas. Thus, the computed brightness weight average value (AYw) tends to be located in these areas of interest, thus, taking AYw as the exposure control parameters can more easily obtain normal exposure of the subject positioned in the middle of the picture. The key of this method lies in the design of control rules, which computes and obtains AYw approximating with AY in common scenes; in the special scenes, AYw primarily relies on the areas of interest. The drawbacks of this method are: 1. the corresponding fuzzy logic control rules have to be drawn up according to the prior knowledge of the majority application scenes and typical application environment, and it requires building a scene pattern database inside a camera or a video camera. This implementation procedure is complex; 2. the generated exposure effect is closely associated with the image partition mode and the position of the photograph subject in the picture, this method relies too much on presumption and lacks versatility/universality.
Shuji Shimizu provided a method for reducing under exposure or over exposure for subjects photographed under “special scenes” in the paper “A new algorithm for exposure control based on fuzzy logic for video cameras” (Sony Intelligent System Research Lab, Sony Corporation. “A new algorithm for exposure control based on fuzzy logic for video cameras”, IEEE Transactions on Consumer Electronics, Vol. 38, No. 3, AUGUST 1992). The main idea of this method is to acquire two exposure control parameters H_mean and H_diff through image brightness distribution statistics. These two parameters reflect the area ratio of the “bright region” and “dark region” in a picture and the contrast intensity of the “bright region” and “dark region”; exposure control is carried out through these two parameters based on a set group of exposure compensation rules. With this method, H_mean is defined as the ratio of brightness greater than pixels of AY occupying the number of pixels in the entire picture; H_half is the ratio that brightness is greater than ½ of the pixels of AY occupying the number of pixels in the entire picture; H_twice is the ratio that brightness is as twice as great than the pixels of AY proportionate to the number of pixels in the entire picture; H_diff takes the smaller one among (H_twice−H_mean) and (H_mean−H_half). The exposure compensation rules are designed which are primarily sensitive to the latter. The drawbacks of this method are: 1. it merely provided an exposure compensation method for the “special scenes” which faced against light or faced directly into strong light in automatic exposure, and the application scope is comparatively limited; 2. exposure effect depends on the reliability of these two parameters. Reliability experiments carried out on these two parameters have shown the reliability of H_mean is relatively high, however, this parameter is not the dominant factor controlling compensation; the amount of compensation is primarily decided by H_diff. However, this parameter is very sensitive and is affected by a variety of conditions. For example, a complex and layered background has a huge impact to this parameter. Therefore, this method is more effective for clear and simple scenes, and less effective for complicated scenes.